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I am interested in '''Machine Learning *Ran X., Pan Y., Sun W. and Multi Wang C.. Learn to Select via Hierarchical Gate Mechanism for Aspect-label Learning'''Based Sentiment Analysis.  *On In ''Proceedings of IJCAL 2019.''*Sun W., Wang C..Multi-label Text Classification (MLTC),''' text features can be regarded as '''detailed description of documents''' and label sets can be '''a summarization of documents''': Select Distinct Semantic Understanding for Different Labels[C]. In '''Hybrid Topics''' from text features and label sets by LDA (a method Proceedings of '''topic model''') can effectively mine global label correlations and deeper featuresApWeb-WAIM 2019.  The paper '''"*Sun W., Ran X.. Luo X., and Wang C..An Efficient Framework by Topic Model for Multi-label Text Classification"'''  has been. In accepted in '''Proceedings of IJCNN 2019.'''. *Deep learning For multi-label text classificationXu Y. We utilize '''dilated convolution''' to obtain the '''semantic understanding''' of the text and design a hybrid '''attention mechansim''' for '''different labels''' (Specifically, each label should attend to most relevant textual contents)Ran X.. Sun W.  Firstly,Luo X. we initialize trainable label embeddingsand Wang C. Then After obationing word-level information based on Bi-LSTM, we get semantic understanding of texts based on word-level information by dilated convolution. Finally, we design a hybrid attention Gated Neural Network with Regularized Loss for different labels based on Multi-label embeddingsText Classification.In  Besides, we add ''Proceedings of IJCNN 2019.'label cooccurrence matrix into loss function '''to guide the whole network to learn *Ran X., Pan Y., Sun W. and achieve good results.  The paperWang C.. Modeling More Globally: A Hierarchical Attention Network via Multi-Task Learning for Aspect-Based Sentiment Analysis. In "'''Multi-label Classification: Select Distinct Semantic Understanding Proceedings of Text for Different LabelsDASFAA 2019'''" has been accepted in'''APWEB-WAIM 2019, Chiang Mai, Thailand, Apr.''' *'''GCN (Graph Convolution Network) '''can be used to exploit more complex label correlations on Image Multi22-label Learning.25,  2019: 505-509.

*[https://github.com/XSilverBullet/Multi-label-Paper Related WorksWork]: This page categorizes a list of works of my interest, mainly in Multi-Label Learning.